Cortex parcellation via diffusion data as prior knowledge for the MEG inverse problem

Anne-Charlotte Philippe (1) , Maureen Clerc (1) , Théodore Papadopoulo (1) , Rachid Deriche (1)

1 ATHENA - Computational Imaging of the Central Nervous System

In this paper, we present a new approach to the recovery of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) imaging. This method consists in introducing prior knowledge regarding the anatomical connectivity in the brain to this ill-posed inverse problem. Thus, we perform cortex parcellation via structural information coming from diffusion MRI (dMRI), the only non-invasive modality allowing to have access to the structure of the WM tissues. Then, we constrain, in the MEG inverse problem, sources in the same diffusion parcel to have close magnitude values. Results of our method on MEG simulations are presented and favorably compared with classical source reconstruction methods.

Mots clés: biodiffusion; biological tissues; biomedical MRI; magnetoencephalography; medical image processing; MEG inverse problem; MEG simulation; WM tissue structure; anatomical connectivity; brain; classical source reconstruction method; cortex parcellation; diffusion MRI; diffusion data; diffusion parcel; dipole magnitude recovery; distributed source model; ill-posed inverse problem; magnetoencephalographic imaging; magnitude value; noninvasive modality; prior knowledge; structural information; Equations; Image reconstruction; Inverse problems; Magnetic resonance imaging; Noise; Vectors; dMRI

Domaines: Algorithme et structure de données [cs.DS]

Anne-Charlotte Philippe, Maureen Clerc, Théodore Papadopoulo, Rachid Deriche. Cortex parcellation via diffusion data as prior knowledge for the MEG inverse problem. Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, Apr 2013, San Francisco, United States. pp.994-997, ⟨10.1109/ISBI.2013.6556644⟩. ⟨hal-00858019⟩